Research on the design and implementation of computer experiment cloud platform under the application of big data technology
Đã lưu trong:
| Xuất bản năm: | Applied Mathematics and Nonlinear Sciences vol. 9, no. 1 (2024) |
|---|---|
| Tác giả chính: | |
| Tác giả khác: | , , |
| Được phát hành: |
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
|
| Những chủ đề: | |
| Truy cập trực tuyến: | Citation/Abstract Full Text - PDF |
| Các nhãn: |
Không có thẻ, Là người đầu tiên thẻ bản ghi này!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3191122728 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2444-8656 | ||
| 024 | 7 | |a 10.2478/amns.2023.2.00794 |2 doi | |
| 035 | |a 3191122728 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 100 | 1 | |a Xiao, Dong |u Daqing Normal University, Daqing, Heilongjiang, 163000, China | |
| 245 | 1 | |a Research on the design and implementation of computer experiment cloud platform under the application of big data technology | |
| 260 | |b De Gruyter Brill Sp. z o.o., Paradigm Publishing Services |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In this paper, the computer experiment cloud platform is built using big data technology, and various functions are implemented based on optimizing the platform parameters based on distributed estimation algorithms. The functional and non-functional requirements of the platform are determined, and the overall architecture design and system database design of the platform are carried out. The distributed estimation algorithm and the UDMA algorithm are used to identify the motor model, and the PID controller uses a particle swarm-based algorithm for parameter optimization. The platform system’s requirements are evaluated from environmental and performance perspectives to ensure that it meets the requirements proposed by the requirements analysis. The value of W(Si) is small, and the actual performance load of the platform remains. The performance of the platform function is good and can withstand a large amount of concurrency, and the average value of 90% response time is maintained at 5.8698s, showing an increasing trend. | |
| 653 | |a Big Data | ||
| 653 | |a Algorithms | ||
| 700 | 1 | |a Cai, Zhaohui |u Daqing Normal University, Daqing, Heilongjiang, 163000, China | |
| 700 | 1 | |a Xu, Hemin |u Daqing Normal University, Daqing, Heilongjiang, 163000, China | |
| 700 | 1 | |a Yuan, An |u Daqing Normal University, Daqing, Heilongjiang, 163000, China | |
| 773 | 0 | |t Applied Mathematics and Nonlinear Sciences |g vol. 9, no. 1 (2024) | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3191122728/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3191122728/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |